arXiv:2511.17442v3 Announce Type: replace-cross Abstract: Foundation Models (FMs) are increasingly integrated into remote sensing (RS) pipelines. These models include unimodal vision encoders and multimodal architectures. FMs are adapted to diverse perception tasks, such as image classification, change detection, and visual question answering. However, selecting the most suitable remote sensing foundation model (RSFM) for a specific task remains challenging due to scattered documentation, heterogeneous formats, and complex deployment constraints. To address this, we first introduce the RSFM Da
The proliferation of foundation models in various domains, including remote sensing, necessitates intelligent tools for selection and deployment to overcome complexity.
This development addresses the growing challenge of efficiently utilizing powerful AI models in critical applications like remote sensing, enabling more precise and automated analysis of geographical data.
The arduous manual process of selecting appropriate remote sensing foundation models (RSFMs) for specific tasks can now be automated and optimized through agent-based systems.
- · Remote Sensing industry
- · AI Agents developers
- · Environmental monitoring services
- · Defense and intelligence agencies
- · Manual model selection processes
- · Inefficient remote sensing data analysis
- · Organizations without AI integration skills
Improved efficiency and accuracy in remote sensing applications across diverse sectors.
Accelerated development and adoption of AI-driven solutions for Earth observation and geospatial intelligence.
Enhanced geopolitical and economic advantages for nations and entities proficient in leveraging advanced remote sensing AI.
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Read at arXiv cs.AI